Information theory perspective on network robustness

Schieber, Tiago A.; Carpi, Laura C.; Frery, Alejandro C.; Rosso, Osvaldo A.; Pardalos, Pãnos M.; Ravetti, Martı́n Gómez

doi:10.1016/j.physleta.2015.10.055

Cited by 29 publications

(16 citation statements)

References 31 publications

(34 reference statements)

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“…6a). This strategy is vulnerable, as failure of early events can cause widespread defects51. We considered whether the organization of inducible gene expression in macrophages might compound the deregulation of inflammatory gene expression in cohesin-deficient cells.…”

Section: Resultsmentioning

confidence: 99%

Control of inducible gene expression links cohesin to hematopoietic progenitor self-renewal and differentiation

et al. 2018

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Cohesin is important for 3D genome organization. Nevertheless, even the complete removal of cohesin has surprisingly little impact on steady-state gene transcription and enhancer activity. Here we show that cohesin is required for the core transcriptional response of primary macrophages to microbial signals, and for inducible enhancer activity that underpins inflammatory gene expression. Consistent with a role for inflammatory signals in promoting myeloid differentiation of hematopoietic stem and progenitor cells (HPSCs), cohesin mutations in HSPCs led to reduced inflammatory gene expression and increased resistance to differentiation-inducing inflammatory stimuli. These findings uncover an unexpected dependence of inducible gene expression on cohesin, link cohesin with myeloid differentiation, and may help explain the prevalence of cohesin mutations in human acute myeloid leukemia.

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Section: Resultsmentioning

confidence: 99%

Control of inducible gene expression links cohesin to hematopoietic progenitor self-renewal and differentiation

et al. 2018

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“…The first term compares averaged connectivity node's patterns, corresponding to the so-called graph distance distribution28. Graphs sharing the same distance distribution present the same diameter, average path length (APL) and other connectivity features.…”

Section: Resultsmentioning

confidence: 99%

“…Similarity measures have many uses due to the current widespread use of networks in social sciences, medicine, biology, physics and so on192021222324252627282930. They can help, among many other examples, to discriminate between neurological disorders by quantifying functional and topological similarities31, to find structurally more similar molecules that are more likely to exhibit similar properties, for drug design32, and to quantify changes in temporal evolving networks22.…”

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confidence: 99%

Quantification of network structural dissimilarities

et al. 2017

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Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components.

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“…The shorter diameter of the global network, when compared with the core gene subnetwork, also suggests that non-core genes generally complement the connection among clusters. Although non-core genes are not necessarily essential for cellular function, they could enhance the flexibility and efficiency of networks by providing functional pathway redundancy [ 52 ]. Since the essentiality of a gene is environment-dependent [ 53 ], non-core genes may also include genes which are essential in particular environments.…”

Section: Discussionmentioning

confidence: 99%

Genetic correlation network prediction of forest soil microbial functional organization

Zhao

et al. 2018

The ISME Journal

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Soil ecological functions are largely determined by the activities of soil microorganisms, which, in turn, are regulated by relevant interactions between genes and their corresponding pathways. Therefore, the genetic network can theoretically elucidate the functional organization that supports complex microbial community functions, although this has not been previously attempted. We generated a genetic correlation network based on 5421 genes derived from metagenomes of forest soils, identifying 7191 positive and 123 negative correlation relationships. This network consisted of 27 clusters enriched with sets of genes within specific functions, represented with corresponding cluster hubs. The clusters revealed a hierarchical architecture, reflecting the functional organization in the soil metagenomes. Positive correlations mapped functional associations, whereas negative correlations often mapped regulatory processes. The potential functions of uncharacterized genes were predicted based on the functions of located clusters. The global genetic correlation network highlights the functional organization in soil metagenomes and provides a resource for predicting gene functions. We anticipate that the genetic correlation network may be exploited to comprehensively decipher soil microbial community functions.

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Information theory perspective on network robustness

Abstract: 2016-11-02T18:49:00

Cited by 29 publications

References 31 publications

Control of inducible gene expression links cohesin to hematopoietic progenitor self-renewal and differentiation

Control of inducible gene expression links cohesin to hematopoietic progenitor self-renewal and differentiation

Quantification of network structural dissimilarities

Genetic correlation network prediction of forest soil microbial functional organization

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